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    [60]:
    !pip install lime
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    [1]:
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import seaborn as sns
    import os
    import warnings
    warnings.filterwarnings('ignore')
    [2]:
    df=pd.read_csv("/kaggle/input/deepfake-faces/metadata.csv")
    df.head()
    [2]:
    videoname original_width original_height label original
    0 aznyksihgl.mp4 129 129 FAKE xnojggkrxt.mp4
    1 gkwmalrvcj.mp4 129 129 FAKE hqqmtxvbjj.mp4
    2 lxnqzocgaq.mp4 223 217 FAKE xjzkfqddyk.mp4
    3 itsbtrrelv.mp4 186 186 FAKE kqvepwqxfe.mp4
    4 ddvgrczjno.mp4 155 155 FAKE pluadmqqta.mp4
    [3]:
    def classify_features(df):
    catagorical_feature=[]
    non_catagorical_feature=[]
    discret_feature=[]
    continous_feature=[]
    for column in df.columns:
    if df[column].dtype=="object":
    if df[column].nunique()<10:
    catagorical_feature.append(column)
    else:
    non_catagorical_feature.append(column)
    elif df[column].dtype in ["int64","float64"]:
    if df[column].nunique()<10:
    discret_features.append(column)
    else:
    continous_feature.append(column)
    return catagorical_feature,non_catagorical_feature,discret_feature,continous_feature
    [4]:
    catagorical,non_catagorical,discret,continous=classify_features(df)

    print("catagorical: ",catagorical)
    print("non_catagorical: ",non_catagorical)
    print("discret: ",discret)
    print("continous: ",continous)
    catagorical:  ['label']
non_catagorical:  ['videoname', 'original']
discret:  []
continous:  ['original_width', 'original_height']
    [5]:
    df.isna().sum()
    [5]:
    videoname              0
original_width         0
original_height        0
label                  0
original           16293
dtype: int64
    [6]:
    df=df.fillna("Not Available")
    [7]:
    for i in catagorical:
    print(i,':', df[i].unique())
    print()
    for i in catagorical:
    print(df[i].value_counts())
    print()
    label : ['FAKE' 'REAL']

label
FAKE    79341
REAL    16293
Name: count, dtype: int64
    [8]:
    for i in catagorical:
    plt.figure(figsize=(15,6))
    sns.countplot(x = df[i], data = df, palette = 'hls')
    plt.show()
    Data Balancing

    Data Balancing

    [9]:
    real_df = df[df["label"] == "REAL"]
    fake_df = df[df["label"] == "FAKE"]
    sample_size = 10000

    real_df = real_df.sample(sample_size, random_state=42)
    fake_df = fake_df.sample(sample_size, random_state=42)

    sample_meta = pd.concat([real_df, fake_df])
    [10]:
    from sklearn.model_selection import train_test_split

    Train_set, Test_set = train_test_split(sample_meta,test_size=0.2,random_state=42,stratify=sample_meta['label'])
    Train_set, Val_set = train_test_split(Train_set,test_size=0.3,random_state=42,stratify=Train_set['label'])
    Train_set.shape,Val_set.shape,Test_set.shape
    [10]:
    ((11200, 5), (4800, 5), (4000, 5))
    [11]:
    import cv2

    image_path="/kaggle/input/deepfake-faces/faces_224/"

    image_file=os.listdir(image_path)
    image_file.sort()
    selected_image=image_file[:9]

    plt.figure(figsize=(10,10))

    for index,image_file in enumerate(selected_image):
    image=cv2.imread(os.path.join(image_path,image_file))
    plt.subplot(3,3,index+1)
    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    plt.title(f'Image {index + 1}')
    plt.axis('off')

    plt.show()
    [12]:
    def retreive_dataset(set_name):
    images,labels=[],[]
    for (img, imclass) in zip(set_name['videoname'], set_name['label']):
    images.append(cv2.imread('../input/deepfake-faces/faces_224/'+img[:-4]+'.jpg'))
    if(imclass=='FAKE'):
    labels.append(1)
    else:
    labels.append(0)
    return np.array(images),np.array(labels)
    [13]:
    X_train,y_train=retreive_dataset(Train_set)
    X_val,y_val=retreive_dataset(Val_set)
    X_test,y_test=retreive_dataset(Test_set)
    [14]:
    import tensorflow as tf
    from tensorflow.keras import layers, models
    from functools import partial

    tf.random.set_seed(42)
    2025-09-23 05:46:53.513236: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1758606413.687765      36 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1758606413.739889      36 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
    [15]:
    IMAGE_SIZE=224
    BATCH_SIZE=32
    EPOCHS=10
    [16]:
    from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau

    # Save the best model based on validation loss
    def get_callbacks(name):
    checkpoint_cb = ModelCheckpoint(
    filepath=f'best_model_{name}.h5', # Use '.keras' if using newer TF versions
    monitor='val_loss',
    save_best_only=True,
    save_weights_only=False,
    verbose=1
    )

    # Stop training early if validation loss doesn't improve
    earlystop_cb = EarlyStopping(
    monitor='val_loss',
    patience=3, # Number of epochs with no improvement
    restore_best_weights=True,
    verbose=1
    )

    # Reduce learning rate when validation loss plateaus
    reduce_lr_cb = ReduceLROnPlateau(
    monitor='val_loss',
    factor=0.5,
    patience=5,
    min_lr=1e-6,
    verbose=1
    )

    # Combine all callbacks
    callbacks = [checkpoint_cb, earlystop_cb, reduce_lr_cb]

    return callbacks
    [17]:
    DefaultConv2D = partial(layers.Conv2D, kernel_size=3, padding="same",
    activation="relu", kernel_initializer="he_normal")

    # Model Definition
    model = models.Sequential([
    DefaultConv2D(filters=64, kernel_size=7, input_shape=[IMAGE_SIZE, IMAGE_SIZE, 3]),
    layers.MaxPooling2D(),
    layers.BatchNormalization(),
    DefaultConv2D(filters=128),
    DefaultConv2D(filters=128),
    layers.MaxPooling2D(),
    layers.BatchNormalization(),
    layers.Flatten(),
    layers.Dense(units=128, activation="relu",
    kernel_initializer="he_normal"),
    layers.BatchNormalization(),
    layers.Dropout(0.5),
    layers.Dense(units=64, activation="relu",
    kernel_initializer="he_normal"),
    layers.BatchNormalization(),
    layers.Dropout(0.5),
    layers.Dense(units=1, activation="sigmoid")
    ])
    I0000 00:00:1758606425.304481      36 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13942 MB memory:  -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5
I0000 00:00:1758606425.305187      36 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13942 MB memory:  -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5
    [18]:
    initial_learning_rate = 0.001
    lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate, decay_steps=100000, decay_rate=0.96, staircase=True
    )
    [19]:
    model.compile(
    optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),
    loss="binary_crossentropy",
    metrics=[
    "accuracy",
    tf.keras.metrics.Recall(name='recall'),
    tf.keras.metrics.Precision(name='precision')
    ]
    )
    [20]:
    model.summary()
    Model: "sequential"

    ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv2d (Conv2D)                 │ (None, 224, 224, 64)   │         9,472 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d (MaxPooling2D)    │ (None, 112, 112, 64)   │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization             │ (None, 112, 112, 64)   │           256 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_1 (Conv2D)               │ (None, 112, 112, 128)  │        73,856 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_2 (Conv2D)               │ (None, 112, 112, 128)  │       147,584 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_1 (MaxPooling2D)  │ (None, 56, 56, 128)    │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_1           │ (None, 56, 56, 128)    │           512 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten (Flatten)               │ (None, 401408)         │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 128)            │    51,380,352 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_2           │ (None, 128)            │           512 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 128)            │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 64)             │         8,256 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ batch_normalization_3           │ (None, 64)             │           256 │
│ (BatchNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 64)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 1)              │            65 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

     Total params: 51,621,121 (196.92 MB)

     Trainable params: 51,620,353 (196.92 MB)

     Non-trainable params: 768 (3.00 KB)
    [21]:
    history = model.fit(
    X_train,
    y_train,
    epochs=10,
    callbacks=get_callbacks('custom'),
    batch_size=BATCH_SIZE,
    validation_data=(X_val,y_val),
    verbose=1
    )
    Epoch 1/10

    WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1758606435.790639     101 service.cc:148] XLA service 0x7e9ab4006430 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1758606435.791181     101 service.cc:156]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5
I0000 00:00:1758606435.791225     101 service.cc:156]   StreamExecutor device (1): Tesla T4, Compute Capability 7.5
I0000 00:00:1758606436.312170     101 cuda_dnn.cc:529] Loaded cuDNN version 90300
I0000 00:00:1758606450.879275     101 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

    350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 143ms/step - accuracy: 0.5246 - loss: 0.9344 - precision: 0.5306 - recall: 0.5318
Epoch 1: val_loss improved from inf to 0.79419, saving model to best_model_custom.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 85s 184ms/step - accuracy: 0.5247 - loss: 0.9341 - precision: 0.5306 - recall: 0.5319 - val_accuracy: 0.5221 - val_loss: 0.7942 - val_precision: 0.5798 - val_recall: 0.1604 - learning_rate: 0.0010
Epoch 2/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 149ms/step - accuracy: 0.5587 - loss: 0.7276 - precision: 0.5658 - recall: 0.5567
Epoch 2: val_loss improved from 0.79419 to 0.65845, saving model to best_model_custom.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 61s 175ms/step - accuracy: 0.5587 - loss: 0.7276 - precision: 0.5658 - recall: 0.5567 - val_accuracy: 0.5979 - val_loss: 0.6584 - val_precision: 0.5641 - val_recall: 0.8612 - learning_rate: 0.0010
Epoch 3/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 150ms/step - accuracy: 0.6110 - loss: 0.6655 - precision: 0.6107 - recall: 0.6406
Epoch 3: val_loss did not improve from 0.65845
350/350 ━━━━━━━━━━━━━━━━━━━━ 59s 169ms/step - accuracy: 0.6110 - loss: 0.6655 - precision: 0.6107 - recall: 0.6405 - val_accuracy: 0.5073 - val_loss: 1.3706 - val_precision: 0.6446 - val_recall: 0.0325 - learning_rate: 0.0010
Epoch 4/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 150ms/step - accuracy: 0.6426 - loss: 0.6314 - precision: 0.6413 - recall: 0.6692
Epoch 4: val_loss did not improve from 0.65845
350/350 ━━━━━━━━━━━━━━━━━━━━ 59s 169ms/step - accuracy: 0.6426 - loss: 0.6314 - precision: 0.6413 - recall: 0.6692 - val_accuracy: 0.5367 - val_loss: 0.8703 - val_precision: 0.8359 - val_recall: 0.0913 - learning_rate: 0.0010
Epoch 5/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 150ms/step - accuracy: 0.6529 - loss: 0.6132 - precision: 0.6566 - recall: 0.6607
Epoch 5: val_loss improved from 0.65845 to 0.61754, saving model to best_model_custom.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 61s 175ms/step - accuracy: 0.6529 - loss: 0.6132 - precision: 0.6566 - recall: 0.6608 - val_accuracy: 0.6510 - val_loss: 0.6175 - val_precision: 0.6891 - val_recall: 0.5504 - learning_rate: 0.0010
Epoch 6/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 150ms/step - accuracy: 0.6884 - loss: 0.5886 - precision: 0.6831 - recall: 0.7190
Epoch 6: val_loss improved from 0.61754 to 0.61076, saving model to best_model_custom.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 61s 175ms/step - accuracy: 0.6884 - loss: 0.5885 - precision: 0.6831 - recall: 0.7189 - val_accuracy: 0.6615 - val_loss: 0.6108 - val_precision: 0.7084 - val_recall: 0.5487 - learning_rate: 0.0010
Epoch 7/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 150ms/step - accuracy: 0.7158 - loss: 0.5415 - precision: 0.7098 - recall: 0.7424
Epoch 7: val_loss did not improve from 0.61076
350/350 ━━━━━━━━━━━━━━━━━━━━ 59s 168ms/step - accuracy: 0.7158 - loss: 0.5415 - precision: 0.7098 - recall: 0.7424 - val_accuracy: 0.6233 - val_loss: 0.6756 - val_precision: 0.7236 - val_recall: 0.3992 - learning_rate: 0.0010
Epoch 8/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 149ms/step - accuracy: 0.7532 - loss: 0.4999 - precision: 0.7456 - recall: 0.7787
Epoch 8: val_loss did not improve from 0.61076
350/350 ━━━━━━━━━━━━━━━━━━━━ 59s 168ms/step - accuracy: 0.7532 - loss: 0.4999 - precision: 0.7456 - recall: 0.7787 - val_accuracy: 0.6404 - val_loss: 0.6344 - val_precision: 0.6123 - val_recall: 0.7654 - learning_rate: 0.0010
Epoch 9/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 149ms/step - accuracy: 0.7850 - loss: 0.4459 - precision: 0.7783 - recall: 0.8050
Epoch 9: val_loss did not improve from 0.61076
350/350 ━━━━━━━━━━━━━━━━━━━━ 59s 167ms/step - accuracy: 0.7850 - loss: 0.4458 - precision: 0.7783 - recall: 0.8051 - val_accuracy: 0.5454 - val_loss: 1.8196 - val_precision: 0.7535 - val_recall: 0.1350 - learning_rate: 0.0010
Epoch 9: early stopping
Restoring model weights from the end of the best epoch: 6.
    [22]:
    y_pred = model.predict(X_test)
    125/125 ━━━━━━━━━━━━━━━━━━━━ 6s 43ms/step
    [23]:
    from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix, classification_report, roc_auc_score
    [24]:
    y_test_pred_binary = (y_pred > 0.5).astype(int)
    [25]:
    y_train_pred = model.predict(X_train)
    y_train_pred_binary = (y_train_pred > 0.5).astype(int)
    350/350 ━━━━━━━━━━━━━━━━━━━━ 15s 43ms/step
    [26]:
    from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix, classification_report, roc_auc_score

    train_accuracy = accuracy_score(y_train, y_train_pred_binary)
    print(f"Training Accuracy: {train_accuracy * 100:.2f}%")
    Training Accuracy: 72.52%
    [27]:
    test_accuracy = accuracy_score(y_test, y_test_pred_binary)
    print(f"Test Accuracy: {test_accuracy * 100:.2f}%")
    Test Accuracy: 66.40%
    [28]:
    f1 = f1_score(y_test, y_test_pred_binary)
    print(f"F1 Score: {f1:.4f}")

    precision = precision_score(y_test, y_test_pred_binary)
    print(f"Precison: {precision:.4f}")

    recall = recall_score(y_test, y_test_pred_binary)
    print(f"Recall: {recall:.4f}")

    # Calculate AUC-ROC
    auc_roc = roc_auc_score(y_test, y_test_pred_binary)
    print(f"AUC-ROC: {auc_roc:.4f}")
    F1 Score: 0.6201
Precison: 0.7133
Recall: 0.5485
AUC-ROC: 0.6640
    [29]:
    conf_matrix = confusion_matrix(y_test, y_test_pred_binary)
    print("Confusion Matrix:")
    print(conf_matrix)
    Confusion Matrix:
[[1559  441]
 [ 903 1097]]
    [30]:
    import seaborn as sns
    sns.heatmap(conf_matrix, annot=True)
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.title('Confusion Matrix of custom model')
    plt.tight_layout()
    plt.show()
    [31]:
    class_report = classification_report(y_test, y_test_pred_binary)
    print("Classification Report:")
    print(class_report)
    Classification Report:
              precision    recall  f1-score   support

           0       0.63      0.78      0.70      2000
           1       0.71      0.55      0.62      2000

    accuracy                           0.66      4000
   macro avg       0.67      0.66      0.66      4000
weighted avg       0.67      0.66      0.66      4000
    [32]:
    import matplotlib.pyplot as plt

    # Unpack training history
    hist = history.history

    # Create 2x2 subplot
    plt.figure(figsize=(14, 10))

    # Accuracy
    plt.subplot(2, 2, 1)
    plt.plot(hist['accuracy'], label='Train Accuracy')
    plt.plot(hist['val_accuracy'], label='Val Accuracy')
    plt.title('Accuracy')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.legend()

    # Loss
    plt.subplot(2, 2, 2)
    plt.plot(hist['loss'], label='Train Loss')
    plt.plot(hist['val_loss'], label='Val Loss')
    plt.title('Loss')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()

    # Precision
    plt.subplot(2, 2, 3)
    plt.plot(hist['precision'], label='Train Precision')
    plt.plot(hist['val_precision'], label='Val Precision')
    plt.title('Precision')
    plt.xlabel('Epoch')
    plt.ylabel('Precision')
    plt.legend()

    # Recall
    plt.subplot(2, 2, 4)
    plt.plot(hist['recall'], label='Train Recall')
    plt.plot(hist['val_recall'], label='Val Recall')
    plt.title('Recall')
    plt.xlabel('Epoch')
    plt.ylabel('Recall')
    plt.legend()

    plt.tight_layout()
    plt.show()
    [33]:
    model.save("custom_model_ep10.keras")
    [34]:
    from tensorflow.keras.applications import ResNet50

    base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(IMAGE_SIZE,IMAGE_SIZE,3))

    for layer in base_model.layers:
    layer.trainable = False
    model_resnet50 = models.Sequential()
    model_resnet50.add(base_model)
    model_resnet50.add(layers.GlobalAveragePooling2D())
    model_resnet50.add(layers.Dense(1, activation='sigmoid'))
    Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
94765736/94765736 ━━━━━━━━━━━━━━━━━━━━ 1s 0us/step
    [35]:
    model_resnet50.summary()
    Model: "sequential_1"

    ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ resnet50 (Functional)           │ (None, 7, 7, 2048)     │    23,587,712 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling2d        │ (None, 2048)           │             0 │
│ (GlobalAveragePooling2D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_3 (Dense)                 │ (None, 1)              │         2,049 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

     Total params: 23,589,761 (89.99 MB)

     Trainable params: 2,049 (8.00 KB)

     Non-trainable params: 23,587,712 (89.98 MB)
    [36]:
    from tensorflow.keras import optimizers

    model_resnet50.compile(
    optimizer="adam",
    loss='binary_crossentropy',
    metrics=[
    "accuracy",
    tf.keras.metrics.Recall(name='recall'),
    tf.keras.metrics.Precision(name='precision')
    ]
    )
    [37]:
    history = model_resnet50.fit(
    X_train, y_train,
    epochs=10,
    callbacks=get_callbacks('resnet50'),
    validation_data=(X_val, y_val),
    verbose=1
    )
    Epoch 1/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - accuracy: 0.5700 - loss: 0.6927 - precision: 0.5758 - recall: 0.5795
Epoch 1: val_loss improved from inf to 0.60883, saving model to best_model_resnet50.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 62s 136ms/step - accuracy: 0.5701 - loss: 0.6926 - precision: 0.5759 - recall: 0.5796 - val_accuracy: 0.6702 - val_loss: 0.6088 - val_precision: 0.6588 - val_recall: 0.7063 - learning_rate: 0.0010
Epoch 2/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - accuracy: 0.6648 - loss: 0.6096 - precision: 0.6671 - recall: 0.6752
Epoch 2: val_loss improved from 0.60883 to 0.59250, saving model to best_model_resnet50.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 41s 116ms/step - accuracy: 0.6648 - loss: 0.6096 - precision: 0.6671 - recall: 0.6752 - val_accuracy: 0.6812 - val_loss: 0.5925 - val_precision: 0.6693 - val_recall: 0.7167 - learning_rate: 0.0010
Epoch 3/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - accuracy: 0.6829 - loss: 0.5871 - precision: 0.6847 - recall: 0.6935
Epoch 3: val_loss improved from 0.59250 to 0.58348, saving model to best_model_resnet50.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 41s 116ms/step - accuracy: 0.6829 - loss: 0.5871 - precision: 0.6847 - recall: 0.6935 - val_accuracy: 0.6854 - val_loss: 0.5835 - val_precision: 0.6736 - val_recall: 0.7196 - learning_rate: 0.0010
Epoch 4/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - accuracy: 0.6993 - loss: 0.5720 - precision: 0.7013 - recall: 0.7080
Epoch 4: val_loss improved from 0.58348 to 0.57742, saving model to best_model_resnet50.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 41s 116ms/step - accuracy: 0.6993 - loss: 0.5720 - precision: 0.7013 - recall: 0.7080 - val_accuracy: 0.6915 - val_loss: 0.5774 - val_precision: 0.6803 - val_recall: 0.7225 - learning_rate: 0.0010
Epoch 5/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - accuracy: 0.7122 - loss: 0.5603 - precision: 0.7131 - recall: 0.7225
Epoch 5: val_loss improved from 0.57742 to 0.57307, saving model to best_model_resnet50.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 41s 116ms/step - accuracy: 0.7122 - loss: 0.5603 - precision: 0.7131 - recall: 0.7225 - val_accuracy: 0.6954 - val_loss: 0.5731 - val_precision: 0.6852 - val_recall: 0.7229 - learning_rate: 0.0010
Epoch 6/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - accuracy: 0.7231 - loss: 0.5507 - precision: 0.7246 - recall: 0.7313
Epoch 6: val_loss improved from 0.57307 to 0.56983, saving model to best_model_resnet50.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 41s 116ms/step - accuracy: 0.7231 - loss: 0.5507 - precision: 0.7246 - recall: 0.7313 - val_accuracy: 0.6967 - val_loss: 0.5698 - val_precision: 0.6867 - val_recall: 0.7233 - learning_rate: 0.0010
Epoch 7/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - accuracy: 0.7291 - loss: 0.5426 - precision: 0.7308 - recall: 0.7366
Epoch 7: val_loss improved from 0.56983 to 0.56737, saving model to best_model_resnet50.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 41s 117ms/step - accuracy: 0.7291 - loss: 0.5426 - precision: 0.7307 - recall: 0.7365 - val_accuracy: 0.7008 - val_loss: 0.5674 - val_precision: 0.6905 - val_recall: 0.7279 - learning_rate: 0.0010
Epoch 8/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - accuracy: 0.7341 - loss: 0.5357 - precision: 0.7358 - recall: 0.7413
Epoch 8: val_loss improved from 0.56737 to 0.56550, saving model to best_model_resnet50.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 41s 117ms/step - accuracy: 0.7341 - loss: 0.5357 - precision: 0.7358 - recall: 0.7412 - val_accuracy: 0.7006 - val_loss: 0.5655 - val_precision: 0.6916 - val_recall: 0.7242 - learning_rate: 0.0010
Epoch 9/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - accuracy: 0.7386 - loss: 0.5296 - precision: 0.7395 - recall: 0.7468
Epoch 9: val_loss improved from 0.56550 to 0.56408, saving model to best_model_resnet50.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 41s 117ms/step - accuracy: 0.7385 - loss: 0.5295 - precision: 0.7395 - recall: 0.7468 - val_accuracy: 0.7000 - val_loss: 0.5641 - val_precision: 0.6920 - val_recall: 0.7208 - learning_rate: 0.0010
Epoch 10/10
350/350 ━━━━━━━━━━━━━━━━━━━━ 0s 81ms/step - accuracy: 0.7415 - loss: 0.5241 - precision: 0.7416 - recall: 0.7513
Epoch 10: val_loss improved from 0.56408 to 0.56300, saving model to best_model_resnet50.h5
350/350 ━━━━━━━━━━━━━━━━━━━━ 41s 116ms/step - accuracy: 0.7415 - loss: 0.5241 - precision: 0.7416 - recall: 0.7512 - val_accuracy: 0.7015 - val_loss: 0.5630 - val_precision: 0.6939 - val_recall: 0.7208 - learning_rate: 0.0010
Restoring model weights from the end of the best epoch: 10.
    [38]:
    y_pred = model_resnet50.predict(X_test)
    125/125 ━━━━━━━━━━━━━━━━━━━━ 14s 78ms/step
    [39]:
    y_test_pred_binary = (y_pred > 0.5).astype(int)
    [40]:
    y_train_pred = model_resnet50.predict(X_train)
    y_train_pred_binary = (y_train_pred > 0.5).astype(int)
    350/350 ━━━━━━━━━━━━━━━━━━━━ 28s 79ms/step
    [41]:
    train_accuracy = accuracy_score(y_train, y_train_pred_binary)
    print(f"Training Accuracy: {train_accuracy * 100:.2f}%")
    Training Accuracy: 75.61%
    [42]:
    test_accuracy = accuracy_score(y_test, y_test_pred_binary)
    print(f"Test Accuracy: {test_accuracy * 100:.2f}%")
    Test Accuracy: 71.15%
    [43]:
    f1 = f1_score(y_test, y_test_pred_binary)
    print(f"F1 Score: {f1:.4f}")

    precision = precision_score(y_test, y_test_pred_binary)
    print(f"Precison: {precision:.4f}")

    recall = recall_score(y_test, y_test_pred_binary)
    print(f"Recall: {recall:.4f}")
    F1 Score: 0.7126
Precison: 0.7098
Recall: 0.7155
    [44]:
    conf_matrix = confusion_matrix(y_test, y_test_pred_binary)
    print("Confusion Matrix:")
    print(conf_matrix)
    Confusion Matrix:
[[1415  585]
 [ 569 1431]]
    [45]:
    import seaborn as sns
    sns.heatmap(conf_matrix, annot=True)
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.title('Confusion Matrix of custom model')
    plt.tight_layout()
    plt.show()
    [46]:
    class_report = classification_report(y_test, y_test_pred_binary)
    print("Classification Report:")
    print(class_report)
    Classification Report:
              precision    recall  f1-score   support

           0       0.71      0.71      0.71      2000
           1       0.71      0.72      0.71      2000

    accuracy                           0.71      4000
   macro avg       0.71      0.71      0.71      4000
weighted avg       0.71      0.71      0.71      4000
    [47]:
    import matplotlib.pyplot as plt

    # Unpack training history
    hist = history.history

    # Create 2x2 subplot
    plt.figure(figsize=(14, 10))

    # Accuracy
    plt.subplot(2, 2, 1)
    plt.plot(hist['accuracy'], label='Train Accuracy')
    plt.plot(hist['val_accuracy'], label='Val Accuracy')
    plt.title('Accuracy')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.legend()

    # Loss
    plt.subplot(2, 2, 2)
    plt.plot(hist['loss'], label='Train Loss')
    plt.plot(hist['val_loss'], label='Val Loss')
    plt.title('Loss')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()

    # Precision
    plt.subplot(2, 2, 3)
    plt.plot(hist['precision'], label='Train Precision')
    plt.plot(hist['val_precision'], label='Val Precision')
    plt.title('Precision')
    plt.xlabel('Epoch')
    plt.ylabel('Precision')
    plt.legend()

    # Recall
    plt.subplot(2, 2, 4)
    plt.plot(hist['recall'], label='Train Recall')
    plt.plot(hist['val_recall'], label='Val Recall')
    plt.title('Recall')
    plt.xlabel('Epoch')
    plt.ylabel('Recall')
    plt.legend()

    plt.tight_layout()
    plt.show()
    [48]:
    model.save("resnet50_model_ep10.keras")
    [65]:
    import cv2
    import numpy as np
    from lime import lime_image
    from skimage.segmentation import mark_boundaries
    import matplotlib.pyplot as plt
    [66]:
    Selection deleted
    # Create LIME explainer
    lime_explainer = lime_image.LimeImageExplainer()

    def preprocess_frame(frame):
    frame = cv2.resize(frame, (IMG_SIZE, IMG_SIZE))
    frame = frame.astype("float32") / 255.0
    return frame

    def explain_frame(frame):
    # frame: (224,224,3) in [0-255]
    def predict_fn(images):
    images = np.array(images) / 255.0 # normalize
    return model.predict(images)

    explanation = lime_explainer.explain_instance(
    frame,
    predict_fn,
    top_labels=1,
    hide_color=0,
    num_samples=1000
    )
    top_label = explanation.top_labels[0]
    temp, mask = explanation.get_image_and_mask(
    top_label,
    positive_only=False,
    num_features=10,
    hide_rest=False
    )
    plt.imshow(mark_boundaries(temp / 255.0, mask))
    plt.axis("off")
    plt.show()


    [67]:
    Selection deleted
    def predict_video(video_path, show_explanations=False):
    cap = cv2.VideoCapture(video_path)
    preds = []
    count = 0
    while cap.isOpened() and count < MAX_FRAMES:
    ret, frame = cap.read()
    if not ret:
    break
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    frame_resized = preprocess_frame(frame) # normalized [0-1]
    frame_input = np.expand_dims(frame_resized, axis=0) # shape (1,224,224,3)
    pred = model.predict(frame_input, verbose=0)[0][0]
    preds.append(pred)
    # Show LIME explanation every 10th frame
    if show_explanations and count % 10 == 0:
    print(f"Explaining frame {count}...")
    explain_frame((frame_resized * 255).astype(np.uint8)) # back to [0-255]
    count += 1

    cap.release()
    avg_score = np.mean(preds) if preds else 0.0
    return ("FAKE" if avg_score > 0.5 else "REAL"), avg_score


    [68]:
    # --- Parameters ---
    video_path = "/kaggle/input/deepfake-detection-challenge/train_sample_videos/abqwwspghj.mp4"
    IMG_SIZE = 224
    MAX_FRAMES = 30
    [69]:
    # --- Run prediction ---
    label, score = predict_video(video_path, show_explanations=True)
    print(f"Prediction: {label} (confidence: {score:.4f})")

    Explaining frame 0...
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    Prediction: FAKE (confidence: 0.5130)
    [70]:
    Selection deleted
    model.save("deepfake_resnet50.h5")
    [72]:
    from IPython.display import FileLink

    # This will create the download link for your file
    FileLink("/kaggle/working/deepfake_resnet50.h5")
    [72]:
    /kaggle/working/deepfake_resnet50.h5
    [ ]:

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